H2 and Fuel Cells. Book of Abstracts. Montreal (Canada) 23-25 July 2025
Bibliographic record
Abstract
The future of energy is being reshaped by a collective commitment to sustainability, innovation, and international collaboration. It is with great enthusiasm that we present this conference booklet, a reflection of the global effort to accelerate the deployment of hydrogen and fuel cell technologies in the pursuit of a cleaner and more equitable world. This event is part of the H2 Excellence program, a European-Canadian partnership dedicated to fostering excellence in green hydrogen and fuel cell technologies through advanced training, research, and transnational cooperation. It brings together scientists, engineers, educators, and entrepreneurs from diverse backgrounds who share a common goal: to make hydrogen energy not only technically feasible, but also affordable, safe, and widely accessible. The speakers featured in this booklet represent a rich tapestry of expertise, vision, and passion. Their contributions span from cutting-edge scientific developments and policy frameworks to innovative educational platforms and real-world industrial applications. We are especially proud to highlight initiatives that bridge academia and industry, and that promote knowledge mobilization across borders and disciplines. We invite you to explore the pages that follow with curiosity and purpose. May they ignite new insights, meaningful partnerships, and a renewed sense of urgency to address the energy challenges of our time. Welcome to the future of hydrogen. Welcome to the H2 Excellence experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.334 | 0.147 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".